- A model of the electron density of a two dimensional crystal.
The study of systems like this may provide insight into the realisations of quantum computers.
High-level computing is embedded in nearly all research activities throughout the sciences and engineering. Scientific computing is driven on the one hand by the ability to collect enormous datasets, such as from the Large Hadron Collider at CERN, or from massive deployment of environmental sensors, or gene banks, or medical records. Accessing and interpreting such data is becoming an essential part of scientific work in many fields. In parallel, our needs and ability to simulate and model have grown: this work ranges through ab initio methods in chemistry and materials science, in environmental modelling, in genomics, cell biology, and pharmacology.
It should always be remembered that the increase in computational power has come as much from the improvement in algorithms as from Moore's law, so a piecemeal focus on hardware issues is only part of the agenda. Nowadays, the University must be able to project a strategic view across the subject: computational science is in fact a core skill that we possess and can be used imaginatively to progress other agendas: immediate key examples are environmental science and computational biology.
The focus of this initiative is upon teaching, mostly at the M Phil or Part III level, but targeted at a 1+3 post-doctoral regime. It will enable the linking together of research projects in widely separated departments, encouraging sharing of resource, consolidating intellectual activities, and transmitting skills. The Centre will manage high-level computational resources, including HPCF and CamGrid, with the support of the UCS and a focus on external relations.
The objective of this development programme is focused on the need for infrastructure, posts and the acquisition of space to house an interactive community of researchers from a wide range of different disciplines.